The industry has come a long way from analog CCTV video surveillance systems. The days of a few low-resolution cameras being monitored by a security guard at a desk are becoming rarer in mid-sized to enterprise organizations. Putting the cameras on an enterprise network and treating the video like any other data gives us endless possibilities of what we can do with this powerful and complex information. We witnessed the rise of video content analysis (VCA) technology, or video analytics, in the early 2000s in response to the growth of cameras and general surveillance, spurred on by the emergence of IP cameras, the falling costs of data storage and IT infrastructure, a reactive security posture to a changing threat landscape, and the quick realization that traditional monitoring approaches couldn't keep pace with the growth in video data. The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning.

When we see two people meet, we can often predict what happens next: A handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory(CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

Machine learning and AI seem to be the way of the future. These techniques can help software developers create powerful applications that crunch data, analyze trends, and offer solutions that a developer may not even consider. But getting into machine learning is no easy task. You need to have a background in programming and your algorithm skills need to be at least somewhat competent. Python Machine Learning is one detailed book that covers machine learning from the angle of 3rd party Python libraries.

As a boy growing up in Wales, Dr. Steve Jones was fascinated by solving puzzles and finding solutions to challenging problems. As head of biometrics for the BC Cancer Agency, Dr. Jones arrives at his lab in the morning intent on saving another life. He uses information from the DNA sequencing of tumours followed by computational analysis to find the precise drug to attack and destroy that specific tumour. It's a high-tech approach that helps find solutions in some of the hardest cases. "We've done a number of analyses where the outcome has been able to improve the drug choice and help the patient," says Dr. "If we can introduce machine learning," he says, referring to artificial intelligence, "we can do this even better."

NEC Corporation and the University of Tokyo recently announced the commencement of a comprehensive industry-academia alliance for strengthening innovation in Japan. This alliance differs from others in that it has been created under an organization-to-organization agreement to promote comprehensive collaboration, including the sharing of visions and issues from the fundamental research phase to the commercialization of research results phase, the consideration of social acceptance of research results, and the development of human resources for the future. Among the first actions by this industry-academia alliance for innovation will be considering the magnitude of the impact of AI on society, to conclude the NEC/University of Tokyo Partnership Agreement for Future AI Research and Education in the field of Strategic Artificial Intelligence (AI), and to commence specific activities. Conventional industry-academia alliances have only engaged in limited, small-scale joint research related to individual technologies. However, in order to strengthen Japan's innovation and promote meaningful social changes, it will be imperative to carry out investigations on implications for ethics and legal systems with a focus on commercialization, as well as to create new collaborative alliances that are large in scale and cover broad areas, including human resource development.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Please introduce yourself and your Weps to our readers! We are a team of 20-somethings from Estonia who are behind Weps, the artificial intelligence chatbot website builder that works on any device. Our users can create websites by interacting with Weps in an already familiar messenger-like interface. We were just accepted into Axel Springer Plug and Play accelerator this September and are looking forward to grow together with their help! How did you get the idea to Weps?

My learning preference is to watch lecture videos and thankfully there are several excellent courses online.

When it comes to the future of diagnosing and treating cancer, computers – not humans – could hold the key to delivering the best quality precision medicine. A new study out of the Stanford University School of Medicine has found that computers can be trained to more accurately assess slides of lung cancer tissue than pathologists. "Pathology as it is practiced now is very subjective," said Michael Snyder, PhD, professor and chair of genetics. "Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces the subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."